[Temporal stage and method selection of tree species classification based on GF-2 remote sensing image]

It's important to master tree species composition and distribution in forests for the study of forest ecosystems. To promote the application of domestic Gaofen data in the classification of tree species and to explore the effects of different combining images, classification features and classifier on tree species classification results, three kinds of single temporal data and four kinds of multi-temporal data were constructed. Based on three GF-2 images, according to the multi-scale segmentation, C5.0 feature optimization as well as two classifiers including support vector machine (SVM) and random forest (RF), we finished the object-based classification of eight tree species of different temporal and feature dimensions respectively, and finally achieved good results with overall accuracy between 63.5% and 83.5% and the Kappa coefficient between 0.57 and 0.81. The results showed that the choice of temporal stage would affect the classification results. The results based on multi-temporal were generally better than that on single temporal stage. There were obvious precision differences between different image combinations of multi-temporal as well as different single temporal stage. It is notable that feature optimization played a positive role in the improvement of classification accuracy. SVM was relatively stable across different temporal and feature dimensions, which should be given priority when single temporal and classification features are difficult to distinguish tree species directly, while it should be noted that SVM was easy to overfit. RF was not easy to overfit, but it was more dependent on the quality of classification features and would get good results under favorable image combination.